This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline—the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)—was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.

本文报道了一项研究，该研究旨在通过应用严格的计算因果发现 (CCD) 方法和观察数据来阐明纵向队列警官中创伤后应激 (PTS) 的复杂病因。使用了现有的观察数据集，其中包含 207 名警察的样本，这些警察在进入警察学院培训时被招募。在学院培训期间，参与者在基线时接受了一套全面的临床、自我报告、遗传、神经内分泌和生理指标的评估，然后在培训完成后 12 个月进行了重新评估。数据处理管道——精神病学计算因果发现协议 (PCCDP)— 应用于该数据集以确定 PTS 严重程度的因果模型。发现了一个包含 146 个变量和 345 个双变量关系的因果模型。该模型揭示了警察服务 12 个月时导致 PTS 的 5 个直接原因和 83 个因果途径（四个或更少）。直接原因包括组氨酸脱羧酶 (HDC)和盐皮质激素受体 (MR)的单核苷酸多态性 (SNP )基因、训练期间低感知威胁背景下的听觉惊吓、服役第一年对事件暴露的围创伤痛苦以及服役 1 年培训期间的一般症状严重程度。CCD 方法的应用可以确定一组警官中与 PTS 复杂病因相关的变量和途径。这些知识可能会为治疗和预防与重大事件相关的 PTS 提供新的方法。